Generating gridded agricultural gross domestic product for Brazil : A comparison of methodologies
This paper examines two new methods to generate gridded agricultural Gross Domestic Product (GDP) and compares the results with a traditional method. In the case of Brazil, these two new methods of spatial disaggregation and cross-entropy outperform the prediction of agricultural GDP from the tradit...
| Autores principales: | , , , , , |
|---|---|
| Formato: | Artículo preliminar |
| Lenguaje: | Inglés |
| Publicado: |
World Bank
2019
|
| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/147075 |
| _version_ | 1855519062084288512 |
|---|---|
| author | Thomas, Timothy S. You, Liangzhi Wood-Sichra, Ulrike Ru, Yating Blankespoor, Brian Kalvelagen, Erwin |
| author_browse | Blankespoor, Brian Kalvelagen, Erwin Ru, Yating Thomas, Timothy S. Wood-Sichra, Ulrike You, Liangzhi |
| author_facet | Thomas, Timothy S. You, Liangzhi Wood-Sichra, Ulrike Ru, Yating Blankespoor, Brian Kalvelagen, Erwin |
| author_sort | Thomas, Timothy S. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | This paper examines two new methods to generate gridded agricultural Gross Domestic Product (GDP) and compares the results with a traditional method. In the case of Brazil, these two new methods of spatial disaggregation and cross-entropy outperform the prediction of agricultural GDP from the traditional method that distributes agricultural GDP using rural population. The paper finds that the best prediction method is spatial disaggregation using a regression approach for all the key crops and contributors to agricultural GDP. However, the issue of degrees of freedom is an important limiting factor, as the approach requires sufficient subnational data. The cross-entropy method with readily available spatially distributed crop, livestock, forest, and fish allocation far outperforms the traditional method, at least in the case of Brazil, and can operate with nationaland/or subnational-level data. |
| format | Artículo preliminar |
| id | CGSpace147075 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2019 |
| publishDateRange | 2019 |
| publishDateSort | 2019 |
| publisher | World Bank |
| publisherStr | World Bank |
| record_format | dspace |
| spelling | CGSpace1470752024-10-25T07:54:37Z Generating gridded agricultural gross domestic product for Brazil : A comparison of methodologies Thomas, Timothy S. You, Liangzhi Wood-Sichra, Ulrike Ru, Yating Blankespoor, Brian Kalvelagen, Erwin gross agricultural product spatial data regional accounting spatial distribution agriculture gross national product This paper examines two new methods to generate gridded agricultural Gross Domestic Product (GDP) and compares the results with a traditional method. In the case of Brazil, these two new methods of spatial disaggregation and cross-entropy outperform the prediction of agricultural GDP from the traditional method that distributes agricultural GDP using rural population. The paper finds that the best prediction method is spatial disaggregation using a regression approach for all the key crops and contributors to agricultural GDP. However, the issue of degrees of freedom is an important limiting factor, as the approach requires sufficient subnational data. The cross-entropy method with readily available spatially distributed crop, livestock, forest, and fish allocation far outperforms the traditional method, at least in the case of Brazil, and can operate with nationaland/or subnational-level data. 2019-12-13 2024-06-21T09:11:03Z 2024-06-21T09:11:03Z Working Paper https://hdl.handle.net/10568/147075 en Open Access World Bank Thomas, Timothy S.; You, Liangzhi; Wood-Sichra, Ulrike; Ru, Yating; Blankespoor, Brian; and Kalvelagen, Erwin. 2019. Generating gridded agricultural gross domestic product for Brazil : A comparison of methodologies. Policy Research Working Paper 8985. https://doi.org/10.1596/1813-9450-8985 |
| spellingShingle | gross agricultural product spatial data regional accounting spatial distribution agriculture gross national product Thomas, Timothy S. You, Liangzhi Wood-Sichra, Ulrike Ru, Yating Blankespoor, Brian Kalvelagen, Erwin Generating gridded agricultural gross domestic product for Brazil : A comparison of methodologies |
| title | Generating gridded agricultural gross domestic product for Brazil : A comparison of methodologies |
| title_full | Generating gridded agricultural gross domestic product for Brazil : A comparison of methodologies |
| title_fullStr | Generating gridded agricultural gross domestic product for Brazil : A comparison of methodologies |
| title_full_unstemmed | Generating gridded agricultural gross domestic product for Brazil : A comparison of methodologies |
| title_short | Generating gridded agricultural gross domestic product for Brazil : A comparison of methodologies |
| title_sort | generating gridded agricultural gross domestic product for brazil a comparison of methodologies |
| topic | gross agricultural product spatial data regional accounting spatial distribution agriculture gross national product |
| url | https://hdl.handle.net/10568/147075 |
| work_keys_str_mv | AT thomastimothys generatinggriddedagriculturalgrossdomesticproductforbrazilacomparisonofmethodologies AT youliangzhi generatinggriddedagriculturalgrossdomesticproductforbrazilacomparisonofmethodologies AT woodsichraulrike generatinggriddedagriculturalgrossdomesticproductforbrazilacomparisonofmethodologies AT ruyating generatinggriddedagriculturalgrossdomesticproductforbrazilacomparisonofmethodologies AT blankespoorbrian generatinggriddedagriculturalgrossdomesticproductforbrazilacomparisonofmethodologies AT kalvelagenerwin generatinggriddedagriculturalgrossdomesticproductforbrazilacomparisonofmethodologies |